A Transformer-based Approach for Abstractive Summarization of Requirements from Obligations in Software Engineering Contracts
Chirag Jain, Preethu Rose Anish, A.D. Singh, Smita Ghaisas
Abstract
Software Engineering (SE) contracts are a valuable source of software requirements. Seed requirements derived from SE contracts can provide a starting point to the Requirements Engineering (RE) phase. To extract such a seed however, a correct interpretation of contracts text is crucial. A major challenge with contracts text interpretation is that the text is lengthy, convoluted, and it incorporates a complex Legalese. If a summary of the high-level requirements from obligations present in SE contracts is available to the requirement analysts in a language that is comprehensible to them, they can use this seed requirements knowledge to ask the right questions to the stakeholders. In this paper, we propose an approach for summarizing the requirements present in obligations in a language comprehensible to requirement analysts. We use the principles of Prompt Engineering to prompt GPT-3 to generate summaries for training Natural Language Generation (NLG) models for generating SE-specific summaries. Experiments using NLG models such as BART, GPT-2, T5, and Pegasus indicate that Pegasus generates the most accurate summaries with the highest ROUGE score as compared to other models.